Machine learning (ML) models have been widely used to make predictions.

Reward machines are an established tool for dealing with reinforcement learning problems in which rewards are sparse and depend on complex sequences of actions. However, existing algorithms for learning reward machines assume an overly idealized setting where rewards have to be free of noise. To overcome this practical limitation, we introduce a novel type of reward machines, called stochastic reward machines, and an algorithm for learning them. Our algorithm, based on constraint solving, learns minimal stochastic reward machines from the explorations of a reinforcement learning agent. This algorithm can easily be paired with existing reinforcement learning algorithms for reward machines and guarantees to converge to an optimal policy in the limit. We demonstrate the effectiveness of our algorithm in two case studies and show that it outperforms both existing methods and a naive approach for handling noisy reward functions.

Reward Models (RMs) are key components for evaluating and guiding language model outputs. However, traditional scalar RMs often struggle with incorporating contextual and background information during inference, leading to incomplete evaluations. Generative RMs (GRMs) attempt to address these limitations by generating intermediate reasoning steps. Yet, their uncontrolled black-box nature and inefficiency due to sequential decoding hinder their industrial deployment. Industrial scenarios, such as search and recommendation systems, often involve single-domain tasks requiring evaluation along specific dimensions. In such contexts, diagnosing "bad cases" necessitates structured feedback to identify and optimize dimension-specific issues. In this paper, we propose the Structural Reward Model (SRM), a modular and interpretable framework integrating side-branch models as auxiliary feature generators. By introducing fine-grained dimensions, SRMs enable interpretable and efficient evaluation, facilitating targeted diagnostics and optimization. This structured approach ensures adaptability and scalability for industrial applications. Through comprehensive experiments, we demonstrate that SRMs outperform scalar RMs and GRMs in robustness and alignment with human preferences. The modular design further supports efficient optimization for practical scenarios, allowing SRM to provide a practical reward modeling solution for industry.

Multi-subject fMRI data is critical for evaluating the generality and validity of findings across subjects, and its effective utilization helps improve analysis sensitivity. We develop a shared response model for aggregating multi-subject fMRI data that accounts for different functional topographies among anatomically aligned datasets. Our model demonstrates improved sensitivity in identifying a shared response for a variety of datasets and anatomical brain regions of interest. Furthermore, by removing the identified shared response, it allows improved detection of group differences. The ability to identify what is shared and what is not shared opens the model to a wide range of multi-subject fMRI studies.

Effective decision-making in Large Language Models (LLMs) is essential for handling intricate tasks. However, existing approaches prioritize performance but often overlook the balance between effectiveness and computational cost. To address this, we first introduce the 3E Criteria to systematically assess the cost-effectiveness of search strategies, revealing that existing methods often trade significant efficiency for marginal performance gains. To improve LLM decision-making while maintaining efficiency, we propose the Speculative Reward Model (SRM), a plug-and-play framework that seamlessly integrates with existing search strategies. Specifically, SRM employs an external reward assigner to predict optimal actions, reducing reliance on LLMs' internal self-evaluation. And a speculative verification mechanism is used to prune suboptimal choices and guide the search toward more promising steps. We evaluate SRM on several complex decision-making tasks including mathematical reasoning, planning and numerical reasoning in specialized domains. Experimental results show that SRM reduces costs to 1/10 of the original search framework on average while maintaining effectiveness.

Understanding how deep learning models represent data is currently difficult due to the limited number of methodologies available. This paper demonstrates a versatile and novel visualisation tool for determining the axis alignment of embedded data at any layer in any deep learning model. In particular, it evaluates the distribution around planes defined by the network's privileged basis vectors. This method provides both an atomistic and a holistic, intuitive metric for interpreting the distribution of activations across all planes. It ensures that both positive and negative signals contribute, treating the activation vector as a whole. Depending on the application, several variations of this technique are presented, with a resolution scale hyperparameter to probe different angular scales. Using this method, multiple examples are provided that demonstrate embedded representations tend to be axis-aligned with the privileged basis. This is not necessarily the standard basis, and it is found that activation functions directly result in privileged bases. Hence, it provides a direct causal link between functional form symmetry breaking and representational alignment, explaining why representations have a tendency to align with the neuron basis. Therefore, using this method, we begin to answer the fundamental question of what causes the observed tendency of representations to align with neurons. Finally, examples of so-called grandmother neurons are found in a variety of networks. This work aims to better understand how artificial neural networks represent human-interpretable concepts embedded in their hidden layers. Introductory texts often state that individual artificial neurons may respond to distinct real-world signals. This may be a visual neuron that responds to the presence of fur, while another responds to grass. This has been termed a neural "local coding scheme" (Foldiak & Endres, 2008), "grandmother neurons" (Gross, 2002; Connor, 2005), "gnostic neurons" (Konorski, 1968) and sometimes "one-hot encoding" -- depending on the research field. It is unclear whether trained artificial neural networks produce this structure or whether this is an oversimplification. This work provides a versatile new tool and evidence to aid in determining this fundamental question. Samples provided to a neural network are represented as vectors of activations. These are then typically transformed through a series of affine and non-linear steps to achieve the desired result of training. The activation vectors are frequently decomposed into a particular basis for applying the non-linearities.